A laser scanner collects data about its surroundings by collecting thousands, millions or even billions of points of three-dimensional position information. One innovative laser scanner system for collecting such points is described in U.S. Pat. No. 5,988,862, which is incorporated herein by reference. The points, which together are called a point cloud when viewed on or manipulated by a computer, can be collected so densely that they effectively re-create a scene like a photograph. This allows laser scanner users to use the point clouds to view scenes and collect measurements from the point cloud.
While well-known systems allow a user to view the point cloud from various eye points, there are currently no systems that allow a user to view a point cloud interactively, as if the user were in the point cloud or in a scene depicted by the point cloud, while also preserving accuracy, within a user specified tolerance, for measurements taken from the point cloud.
Well-known technology for viewing non-point-cloud data, specifically for viewing a collection of photographic images taken from different angles, allows a user to view the images as if the user were in the scene. This technology allows a user to pan left, right, up or down, and zoom in or out. In one well-known embodiment of this technology, a user takes photographs of an environment, such as a room, in various directions from a single point. These photographs, which cover the floor, ceiling and surrounding area, are then stitched together by a computer to form an image map, e.g., a cube map. The number of images taken to create the cube map varies from two, using fish eye lenses, to fifty or more, using a conventional consumer camera. Each side of the cube map is represented by an image that essentially sums up the contributions from all of the original images to form an efficiently renderable representation. The cube map algorithm, when running on the computer, returns an image or texture based on the input of viewing direction selected by a user.
While adapting image maps to point clouds has desirable visualization benefits, it requires some nontrivial modifications to ensure that a user can use the data to measure distances in the digital scene. Because image maps simply ensure that an image is correctly displayed on a computer monitor, known image map technology only maintains data sufficient to ensure that pixels are displayed properly. Therefore, the data is only as accurate as screen resolution requires. But, in the context of point clouds, individual points are measured by the laser scanner to an accuracy that will generally exceed the resolution at which the point cloud data is viewed.